ORIGINAL PAPER

Effect of thermal environment on the temporal,spatial and seasonal occurrence of measlesin Ondo state, Nigeria

Akinyemi Gabriel Omonijo & Andreas Matzarakis &

Olusegun Oguntoke & Clement Olabinjo Adeofun

Received: 28 April 2011 /Revised: 23 August 2011 /Accepted: 24 August 2011 /Published online: 18 September 2011# ISB 2011

Abstract We investigated the temporal and spatial dynamics,as well as the seasonal occurrence of measles in Ondo state,Nigeria, to better understand the role of the thermal environ-ment in the occurrence of the childhood killer disease measles,which ranks among the top ten leading causes of child deathsworldwide. The linkages between measles and atmosphericenvironmental factors were examined by correlating human-biometeorological parameters in the study area with reportedclinical cases of measles for the period 1998–2008. We alsoapplied stepwise regression analysis in order to determine thehuman-biometeorological parameters that lead to statisticalchanges in reported clinical cases of measles. We found thathigh reported cases of measles are associated with the leastpopulated areas, where rearing and cohabitation of livestock/domestic animals within human communities are common.There was a significant correlation (P<0.01) between monthlycases of measles and human-biometeorological parametersexcept wind speed and vapour pressure. High transmission ofmeasles occurred in the months of January to May during

the dry season when human thermal comfort indices arevery high. This highlights the importance of the thermalenvironment in disease demographics since it accounted formore than 40% variation in measles transmission within thestudy period.

Keywords Thermal environment .Measles . Thermalindices . Ondo state . Nigeria

Introduction

Scientific interest in the role of the environment, includingweather and climate, in the dynamics of infectious diseasehas been further stimulated by the growing problems ofemergence and re-emergency of infectious diseases despiteseries of intervention policies worldwide (Lipp et al. 2002).For example, measles has remained a public healthchallenge despite the enormous efforts by the World HealthOrganization (WHO) and United Nations Children’s Fund(UNICEF) towards reducing the global burden of thisdisease. Measles virus (MeV)—a member of the genusMorbillivirus that belongs to the family of Paramyxoviridae(Barrett 1999; McCarthy and Goodman 2010)—is acommon infection in children and is spread by therespiratory route. It is characterised by a prodromal illnessof fever, coryza, cough, and conjunctivitis followed by theappearance of a generalised maculopalular rash.

Genetically and antigenetically, MeV is related closely toviruses that are pathogens of sheep, goat and cattle (Barrett1999; Sheshberadaran et al. 1986); weather patterns areknown to play a significant role in the transmission of suchviruses (Omonijo 2010, 2007; Okoli 2003). Measles virusis assumed to have evolved in an environment where theabove mentioned animals and humans live in close

A. G. Omonijo :A. MatzarakisMeteorological Institute, Albert-Ludwigs-University Freiburg,Werthmannstr. 10,79085 Freiburg, Germany

A. G. OmonijoDepartment of Agricultural Technology, Rufus Giwa Polytechnic,Owo, Ondo State, Nigeria

A. G. Omonijo (*) : C. O. AdeofunDepartment of Environmental Management and Toxicology,University of Agriculture,Abeokuta, Nigeriae-mail: akingab@yahoo.com

O. OguntokeDepartment of Geography, University of Ibadan,Ibadan, Nigeria

Int J Biometeorol (2012) 56:873–885DOI 10.1007/s00484-011-0492-8

proximity (McNeil 1976) after the commencement oflivestock farming and domestication of animals in the earlycentres of civilisation in the Middle East (Furuse et al.2010). Today, measles remains one of the top ten leadingcauses of death globally (Strebel et al. 2003; WHO 2007;GHC 2009) and remains prevalent in many developingcountries, especially in parts of Africa and Asia wheremore than 20 million measles cases are reported annually(WHO 2009).

In the West African sub-region, large and recurrentepidemics associated with high mortality have occurred in2003 in Niger and in 2004 in Nigeria and Chad (Grais et al.2007). In Nigeria, measles is an important cause ofchildhood morbidity and mortality. In January 2007, a totalof 1,346 patients were admitted to hospital due to measles,of which 62% of cases were aged 1–4 years and 23% werebetween 5 and 14 years of age (WHO 2007). Measlesoutbreaks have been increasingly common in Nigeria (WHO2008) despite the adoption of the WHO’s four-prongedstrategy and The National Program on Immunization withthe aim of reducing fatal cases of measles to near zero.

Previous analyses of measles dynamics have shown howseasonality in transmission and birth rates can interact togenerate complex multi-annual outbreak dynamics (Earnet al. 2000; Olsen et al. 1988). The impact of demographicvariations is perhaps best shown in the dynamic transitionfrom annual to biennial cycles of measles outbreaks inEngland and Wales (Finkenstadt and Grenfell 2000;Schaffer and Kot 1985). Measles outbreaks pattern inEngland and Wales are also associated with the schoolpattern, with high transmission at the opening of the schooland lower numbers of cases during the school term andmid-term holidays (Fine and Clarkson 1982). However, insub-Saharan Africa, measles occurrence usually follows aseasonal pattern (Ferrari et al. 2010; Adetunji et al. 2007),which has been attributed to changes in the behaviour of thehost with respect to the prevailing weather conditions(Dowell 2001).

The studies of Egunjobi (1993) and Adetunji et al.(2007) stated that measles in Nigeria sometimes occursimmediately after the end of the rainy season, and oftenreaches epidemic proportions in the dry season duringFebruary, March and April. Also, Ferrari et al. (2008, 2010)reported that measles epidemics outbreaks in Niamey,Niger, coincide with the end of the annual rainy season,and decline at the onset of the rainy season (rain is thedominant seasonal driver in this region), regardless of themagnitude of the outbreak. Therefore, there is possibilitythat environmental atmospheric conditions may trigger theoccurrence and spread of measles. There are few referenceson this topic, mainly because studies on measles occurrencehave not considered environmental atmospheric conditionsas trigger factors. The aim of this paper is to analyse the

temporal, spatial and seasonal transmission of measles withrespect to thermal environment based on human-biometeorological approaches in order to ascertain thesignificant role of human-biometerological variables in thetransmission of the disease because of its seasonal pattern,and to determine the threshold for its occurrence by usingbiometeorological indices.

Data and methods

Ondo State (5° 45′ N–8° 15′ N, 4° 45′ E– 6° 00′ E) issituated in the South-western part of Nigeria (Figs. 1, 2, 3).The state has 18 local government areas (LGAs) with apopulation of about 3,441,024 according to the 2006population census and a land area of about 14,606 km2

(NPC 2006). It has a tropical wet-and-dry climate. OndoState has a mean annual rainfall of about 1,500 mm and2,000 mm in the derived savannah and humid forest zones,respectively (Adefolalu 1997).

The source of the measles data for the period 1998 to2008 used in this study is the Monitoring and EvaluationUnit, Ministry of Health, Akure, Ondo state. The followinglimitations in this data are recognised: (1) not everyonetakes their child or children to hospital for measlestreatment because of the belief that measles cannot becured medically, (2) there is no separation betweensuspected and confirmed measles cases. The informationtherefore may not represent the true number of cases ofmeasles in each local government areas across the state.However, the data present a general picture of theoccurrence of measles in the study area. Meteorologicaldata—air temperature, air relative humidity, wind speed,solar radiation and rainfall—used in this study wereprovided by the Agroclimatological and Ecological Moni-toring Unit, Akure, Ondo State; other meteorological dataused were the vapour pressure calculated by RayManmodel. The sea surface temperatures (SSTs) over the area5°N–5°S, 150°–90°W, known as the Niño3 region in theequatorial Pacific Ocean, obtained from the website http://www.esrl.noaa.gov/psd/data/gridded/ were also included inthis study. SSTs over the Niño3 region have been linked toweather patterns over Nigeria, most especially rainfallpattern as noted by Adedoyin (1989); Wassila and Kingtse(2005); Giannini et al. (2003). In addition, since Checkleyet al. (2000) have linked effects of El Niño to diarrhealdiseases in Peruvian children, it will be necessary to attemptto assess the role of this ENSO indicator (SSTs) in theoccurrence of measles because its seasonal occurrenceusually coincides with periods of dryness and excess heat(dry season) in the study area.

The biometeorological parameters: mean radiant temper-ature (Tmrt) (Matzarakis et al. 2000, 2010), physiologically

874 Int J Biometeorol (2012) 56:873–885

equivalent temperature (PET) (Mayer and Hoppe 1987;VDI 1998; Matzarakis et al. 1999) and Universal ThermalClimate Index (UTCI) (Jendritzky et al. 2009) werecalculated using the RayMan model (Matzarakis et al.2007, 2010). The advantage of biometeorological parame-ters that are indicators of human thermal comfort in anenvironment using just meteorological variables is theinclusion of both meteorological components of the thermalenvironment and physiological components of the humanbody, e.g. activity level, age, and clothing, in theircalculation. Therefore, in this study, biometeorologicalparameters are used to assess the role of environmentalthermal conditions in the occurrence of this disease becauseof its seasonal fluctuation pattern.

A population factor (Pf) and the logarithm of reportedmeasles cases in relation to population (Ncp) (Egunjobi1993; Ferrari et al. 2010) were also determined forobjective comparison between the 18 LGAs in the state.

Pf ¼ Population of LGA;Px

PN

Ncp ¼ 1n Nm

Pf¼ loge Nm

Pf

where x varies from LGA to LGA as shown in Table 1, PNis the population of the least populated LGA (Ondo East),Nm is the approximate number of cases of measles in therespective LGAs over the study period.

In addition, a demographic analysis of measles’ patientswas carried out. For further analysis, the 18 LGAs weredivided along the two eco-climatic zones in the state(Fig. 3) for simplicity and easy handling of the data. Thetemporal pattern of the monthly clinical reported measlescases was then examined and a regression analysisperformed. The relationship between each of themeteorological-biometeorological parameters and monthlyclinical reported measles cases was examined by findingthe correlation between the two categories of datasets. Inorder to identify specific meteorological- biometeorologicalparameters, or their combination, that explains the temporalpattern of measles disease, a stepwise multiple regressionanalysis was conducted.

Stepwise multiple regression

Multiple regression is most often used in cases in which theresponse variable depends on more than one explanatoryvariable, sometimes called an extended linear model. The

Fig. 1 Map of Africa showingthe study area (Ondo State,Nigeria)

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multiple regression equation where there are more than twoexplanatory factors is given below:

Y ¼ b0 þ b1X1 þ b2X2 þ b3X3 þ b4X4 þ b5X5 . . . . . . ::n

The dependent variable, Y (monthly measles value)and independent variables X1, X2, X3, X4, X5, …….n,(meteorological-biometeorological parameters) was regressedusing the above equation; b0 is the interception coefficient;b1, b2, b3, b4, b5 are linear effect parameters or coefficients.Coefficients of this equation were estimated from theexperimental results with SPSS 13.0 (SPSS, Chicago, IL).A regression model is considered to be statistically signifi-cant if the calculated F value is larger than the F distributionvalue at a probability of α.

Analysis of variance (ANOVA), including F-test, P-valueof the models and determination coefficient R2, whichmeasures the goodness-of-fit of regression model at aconfidence limit of 95%, was performed. The larger themagnitude of the t-values and the smaller the magnitude ofthe P-values, the more significant is the correspondingcoefficient (Stephenson 2003; Draper and Smith 1981;Storch and Zwiers 1999).

Results

The reporting patterns of measles cases (Table 1) arecategorised into high (*** Ncp ≥ 3.00); medium [** Ncp:(2.00 – 2.99)]; and low [* Ncp: (1 – 1.99)] classes. Thisclassification was based on the value of the logarithm ofreported measles cases in relation to the population of eachLGA (Table 1). In this study, four LGAs (Akoko SouthEast, Ondo East, Akure North and Idanre) fall into thehigher class of reported measles cases while the middle andlower classes have seven LGAs each.

The trend of measles disease reporting (Fig. 4) inrelation to patient age shows that 41% of the 58,873 caseswere found among patients in the age group of ‘1– 4 years’,while the lowest number of cases occurred among patients15 years and above (8.3%). The percentage of reportingvaries from one local government to another. For example,in the age group < 1 year, the percentage of occurrencevaries from 15.8% to 21.5%. Overall, more than 92% of thereported measles cases in the study area were recorded inthe age group < 14 years. This result shows that there is asignificant variation in the occurrence of measles diseaseamong age groups at the 0.01 probability level (P<0.01).

Fig. 2 Map of Nigeria showing the study area (Ondo State)

876 Int J Biometeorol (2012) 56:873–885

Figure 5 shows the distribution of reported clinical measlesdisease according to patient gender in all 18 LGAs of OndoState. Out of the 58,873 cases recorded within the 11-year(1998–2008) study period, 57.8% were males and 42.2%females. In all the LGAs, males accounted for a higherpercentage of the occurrence of measles except in Owo LGA,where both male and females recorded 50% each. Thevariation in the distribution of the occurrence of the diseasebetween the two sex groups is significant (P<0.01).

Figure 6 shows that the highest reported number ofmeasles cases occurred in the months of January to May,reaching a peak in the month of March (18%), while theleast number of reported cases occurred in the month of

August (2.8%). The pattern of trend shown in Fig. 7 (humidforest) is similar to that of Fig. 6 (derived savannah), exceptfor differences in the magnitude of occurrence of thedisease. In all, 66.2% of measles cases within the studyperiod were recorded in the months of January to May andthe other months accounted for the remaining 33.8%; thisindicates that variation in the monthly pattern is significantat 0.01 probability level (P<0.01).

The findings presented in Table 2 show the positivecorrelation between measles and solar radiation, air tem-perature, SST, mean radiant temperature, physiologicallyequivalent temperature and universal thermal climate indexat the 0.01 probability level for both humid forest and

Fig. 3 Map of Ondo State(study area) showing the twoeco-climatic zones and localgovernment areas (LGAs)

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Table 1 Logarithm of reported clinical measles cases in relation to population of each local government areas (LGAs) in Ondo state, Nigeria

LGA Population Populationfactor (Pf)

Average reported measles casesfor the period 1998 to 2008 (Nm)

Logarithm of measles cases in relationto population of each LGA (Ncp)

Rank

Akoko aSouth East*** 82,426 1.10 361 5.34 1bOndo East*** 74,758 1.00 101 4.62 2aAkure North*** 131,587 1.76 357 3.34 3bIdanre*** 129,024 1.73 256 3.21 4aAkoko North East** 175,409 2.35 622 2.74 5bEse-Odo** 154,978 2.07 205 2.57 6bIle-Oluji/Okeigbo** 172,870 2.31 360 2.55 7bIrele** 145,166 1.94 101 2.38 8aIfedore** 176,327 2.36 257 2.35 9bOdigbo** 230,351 3.08 675 2.11 10aOse** 144,901 1.94 48 2.00 11aAkoko North West* 213,792 2.86 206 1.86 12bOkitipupa* 233,565 3.12 260 1.78 13aAkoko South West* 229,486 3.07 201 1.73 14aOwo* 218,886 2.93 100 1.57 15bOndo West* 283,672 3.79 255 1.46 16aAkure South* 353,211 4.72 884 1.44 17bIlaje* 290,615 3.89 101 1.19 18

*** Ncp ≥ 3.00; ** Ncp: (2.00 – 2.99); * Ncp: (1 – 1.99)

*** Ncp (4 LGAs)=37.32%; ** Ncp (7 LGAs)=37.75%; * Ncp (7 LGAs)=24.93%a LGAs in derived savannah eco-climatic zoneb LGAs in humid forest eco-climatic zone

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Fig. 4 Trend of age character-istics of measles’ patients inthe 18 LGAs of Ondo State,Nigeria

878 Int J Biometeorol (2012) 56:873–885

derived savannah zones. In contrast, the results show a non-significant negative correlation with vapour pressure andwind speed in derived savannah zone, but a non-significantpositive correlation in humid forest zone. Also, a significantnegative correlation exists between measles disease and,relative humidity and rainfall at the 0.01 probability levelfor the two eco-climatic zones in the state.

The results of regression analysis in Table 3 (derivedsavannah) showed that all the regression models (A–E) aresignificant (P<0.001). The coefficient of determination R2

from the regression analysis indicates that combinations ofdifferent meteorological/ biometeorological parametersaccounted for 44.6%, 40.8%, 41.2%, 45.5% and 41.9%variation in the occurrence of measles in models A, B, C, Dand E, respectively.

In Table 4, the regression models (A–E) are significant(P<0.001) like the same models in Table 3. The coefficientof determination R2 in Table 4 shows that meteorological/biometeorological parameters account for 37.7%, 38.3%,37.2%, 40.4%, 37.3% of variation in the occurrence ofmeasles disease in model A, B, C, D and E in the humidforest zone of Ondo State, Nigeria.

To identify the specific meteorological-biometeorologicalparameters, or combination thereof, that are statisticallysignificant, the regression modelling results in Table 3 and 4were adjusted by applying stepwise regression analysis. Thisstep filters out those variables that are not statisticallysignificant in the models in Tables 3 and 4 to leave thefollowing final form for derived savannah and humid forestzones of Ondo State.

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Fig. 5 Trend of gender charac-teristics of measles patients inthe 18 LGAs of Ondo state,Nigeria

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Fig. 6 Monthly trend ofreported clinical cases of measlesfor the period 1998–2008 in thederived savannah zone of OndoState, Nigeria

Int J Biometeorol (2012) 56:873–885 879

& Models with different meteorological/ biometeorologicalparameters (derived Savannah zone)

ASMSL ¼ �2140:42þ 67:55PET

þ 0:50Rfall p < 0:001; R2 ¼ 44:5%� �

BSMSL ¼ � 1978:77þ 63:84UTCI

� 0:68Rfall p < 0:001; R2 ¼ 40:8%� �

CSMSL ¼ � 1453:97

þ 43:13Tmrt p < 0:001; R2 ¼ 39:3%� �

DSMSL ¼ � 2939:20þ 83:17Taþ 3:39RH

þ 2:04SR p < 0:001; R2 ¼ 45:2%� �

ESMSL ¼ � 1453:97

þ 43:13Tmrt p < 0:001; R2 ¼ 39:3%� �

& Models with different meteorological/ biometeorologicalparameters (humid forest zone)

AHMSL ¼ �1356:49þ 35:63PET

þ 15:79SST p < 0:001; R2 ¼ 36:8%� �

BHMSL ¼ � 1842:49

þ 60:09UTCI p < 0:001; R2 ¼ 37:7%� �

CHMSL ¼ � 1243:01þ 29:05Tmrt

þ 14:70SST p < 0:001; R2 ¼ 37:2%� �

DHMSL ¼ � 2339:20þ 1:85SRþ 64:59Ta

þ 4:71RH p < 0:001; R2 ¼ 39:8%� �

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2006 2007 2008

Fig. 7 Monthly trend ofreported clinical cases of measlesfor the period 1998–2008 in thehumid forest zone of Ondo state,Nigeria

Table 2 Coefficient of correlation between measles cases andmeteorological/biometeorological parameters in humid forest andderived savannah zones of Ondo state, Nigeria. SR Solar radiation,RH air relative humidity, Ta air temperature, u wind speed, VP vaporpressure, Rfall rainfall, SST sea surface temperature, (Tmrt) meanradiant temperature, PET physiologically equivalent temperature,UTCI universal thermal climate index

Meteorological/biometeorologicalvariable

Measles(savannah)

Measles(humid)

SR (Wm–2) 0.571** 0.556**

RH (%) −0.278** −0.321**Ta (°C) 0.596** 0.555**

u (ms–1) −0.010 0.026

VP (hPa) −0.091 0.089

Rfall (mm) −0.307** −0.348**SST (°C) 0.339** 0.340**

Tmrt (°C) 0.627** 0.592**

PET (°C) 0.641** 0.586**

UTCI (°C) 0.541** 0.614**

*Correlation significant at the 0.05 probability level, ** correlationsignificant at the 0.01 probability level

880 Int J Biometeorol (2012) 56:873–885

EHMSL ¼ �1243:01þ 29:05Tmrt

þ 14:70SST P < 0:001; R2 ¼ 37:2%� �

where SMSL is the monthly value of measles occurrence in thederived savannah zone of Ondo State; HMSL is the monthlyvalue of measles occurrence in the humid forest zone of OndoState; superscripts A–E identify the model; PET, SST, UTCI,Tmrt, SR, Ta and RH remain as defined previously.

Models A, B, and C in the derived savannah zone of OndoState show that the biometeorological indices PET, UTCI andTmrt accounted for 41.2%, 29.3% and 39.3% of variation,respectively, in the occurrence of measles, while models A, Band C in the humid forest zone show that PET, UTC and Tmrt

accounted for 34.4%, 37.7% and 35.1% of variation,respectively, in the occurrence of measles. In model D, airtemperature explains 35.6% of the variation in the occur-rence of measles in the derived savannah zone, and solarradiation explains 30.9% of the variation in the occurrence ofmeasles disease in the humid forest zone of Ondo State.

The relationship between the trend of meteorological/biometeorological parameters and the occurrence of measlesfor the period 1998–2008 in derived savannah and humidforest zones of Ondo State, Nigeria is shown in Figs. 8 and 9.

The highest transmission of measles is between the monthsof January and May when the meteorological/ biometeoro-logical parameters are high. During this period, the values ofPET ranged between 35°C and 38°C, while that of UTCI isbetween 34°C and 37°C, Tmrt is between 41°C and 44°C andair temperature is 32°C–34°C in the derived savannah zoneof Ondo State.

In the humid forest zone of Ondo State, the value of PETis between 32°C and 35°C, UTCI is 32°C–34°C, Tmrt isbetween 37°C and 40°C, and solar radiation values rangedfrom 153 Wm–2 to 166 Wm–2 during high transmission ofmeasles in this zone.

Discussion

An examination of the occurrence of measles in 18 LGAsof Ondo State, Nigeria, shows that the disease is associatedwith socio-economic, basic hygiene/sanitation and environ-mental conditions in general. It can therefore be inferredthat the observable spatial differences in the number ofmeasles disease occurrences are a reflection of differencesin the level of socio-economic development, basic hygiene/sanitation and environmental conditions within the state.

Table 3 Regression modellingresults with different meteoro-logical/ biometeorologicalparameters for the derivedsavannah zone in Ondo state,Nigeria

aConfidence interval at 95%level for estimated regressioncoefficient

Model terms Regression coefficients 95% CIa P-value

A (Constant) −2,190.664 −2,760.634 to −1,620.694 0.000

PET 65.030 47.767 to 82.293 0.000

Rfall 0.460 0.077 to 0.842 0.019

SST 5.462 −13.639 to 24.563 0.572

B (Constant) −1,996.057 −2,570.011 to −1,422.103 0.000

UTCI 62.623 43.903 to 81.342 0.000

Rfall −0.677 −0.946 to −0.408 0.000

SST 2.357 −18.379 to 23.093 0.822

C (Constant) −1,867.584 −2,414.368 to −1,320.800 0.000

Tmrt 44.388 31.284 to 57.492 0.000

Rfall 0.183 −0.170 to 0.537 0.307

SST 13.234 −5.725 to 32.194 0.170

D (Constant) −3,006.048 −3,982.860 to −2,029.237 0.000

Ta 85.178 47.876 to 122.479 0.000

Rfall 0.225 −0.358 to 0.808 0.447

SST 1.676 −18.509 to 21.861 0.870

SR 2.011 0.329 to 3.693 0.019

RH 2.498 −0.591 to 5.587 0.002

E (Constant) −2,273.840 −3141.210 to −1,406.470 0.000

Tmrt 64.748 26.735 to 102.760 0.001

Rfall 0.123 −0.477 to 0.723 0.685

SST 8.752 −11.575 to 29.080 0.396

RH 1.326 −1.754 to 4.405 0.396

Int J Biometeorol (2012) 56:873–885 881

The rearing of, and co-habitation with, domestic animalssuch as West Africa dwarf goats and sheep, which areassociated with measles virus, is linked with weather andclimatic factors as noted by Omonijo (2007, 2010) andOkoli (2003) and is common practice among women in thefour LGAs (Akoko South East, Ondo East, Akure Northand Idanre) reporting relatively high numbers of cases ofmeasles, this being done mainly to support their familyeconomy. In addition, Akoko South East and Akure North

belong to the derived savannah zone, which is relatively drycompared to the humid forest zone of the state. Rearing ofcattle or cows, which are also a source of measles virus, iscommon in this zone, particularly in Akoko South Eastbecause it shares a border with guinea savannah and hasabundant short grasses on which cattle can feed. The abovesituation is similar to that reported by Furuse et al. (2010),which linked the origin of measles to the beginning ofrearing and co-habitation of livestock animals within the

Table 4 Regression modellingresults with different meteoro-logical/ biometeorologicalparameters for the humid forestzone of Ondo state, Nigeria

aConfidence interval at 95%level for estimated regressioncoefficient

Model terms Regression coefficients 95% CIa P-value

A (Constant) – 1,566.680 – 2,065.962 to – 1,067.399 0.000

PET 43.770 28.420 to 59.120 0.000

Rfall 0.165 −0.075 to 0.405 0.175

SST 12.893 −1.789 to 27.574 0.029

B (Constant) −1,844.128 −2,407.127 to −1,281.129 0.000

UTCI 53.773 35.384 to 72.162 0.000

Rfall −0.049 −0.235 to 0.136 0.599

SST 8.601 −6.623 to 23.825 0.266

C (Constant) −1,271.195 −1,706.531 to −835.858 0.000

Tmrt 30.023 19.278 to 40.767 0.000

Rfall 0.028 −0.179 to 0.235 0.790

SST 14.260 −0.320 to 28.841 0.042

D (Constant) −2,255.376 −3,382.052 to −1,128.699 0.000

SR 1.826 0.509 to 3.143 0.007

RH 3.203 −2.371 to 8.777 0.026

Ta 58.017 22.045 to 93.990 0.002

Rfall 0.051 −0.283 to 0.386 0.763

SST 8.451 −6.834 to 23.736 0.276

E (Constant) −1,488.985 −2,561.911 to −416.059 0.007

RH 1.201 −4.463 to 6.866 0.675

Tmrt 37.454 −2.888 to 77.795 0.009

Rfall 0.003 −0.342 to 0.348 0.985

SST 13.187 −2.229 to 28.604 0.043

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PET,

UT

CI,

Tm

rt, T

a (º

C)

Rep

orte

d cl

inic

al c

ases

of

mea

sles

Month

MSL (Savanna)PETUTCITmrtTa

Fig. 8 Relationship betweentrends of meteorological/biometeorological parametersand occurrence of measles forthe period 1998–2008 in thederived savannah zone of Ondostate, Nigeria

882 Int J Biometeorol (2012) 56:873–885

human community in early centres of civilization in theMiddle East.

Another factor that could explain the spatial differencesin the number of reported cases of measles in the study areais basic hygiene/sanitation principles, as noted by Egunjobi(1993). Since the majority of the inhabitants of these LGAswith high reporting cases of measles are rural dwellers andagrarian farmers, the level of their education is very lowand therefore observing basic hygiene/sanitation principlesdoes not matter to them.

The results of this study showed that the least populatedLGAs recorded the highest reporting cases ofmeasles within thestudy period, which contradicts a report by Fine and Clarkson(1982) that stated that cities in industrialised countries arereservoirs of measles because of their size and population.

The findings of this study show that the age group of14 years and younger is the most affected by measles(91.7%). However, we suggest that the measles immunisa-tion programme should be extended beyond the age of15 years since we could not specify the actual age of thosepatients that are affected with measles in the age group of15 years and above because there may be a shift ofinfection to older age groups as noted in Germany byWichmann et al. (2009). Males are more vulnerable tomeasles, which concurs with the results of several otherstudies on measles (Adeoye et al. 2010; Ernest 2004;Adetunji et al. 2007; Duncan et al. 1999).

The seasonal pattern of measles occurrence in this studyfollows high transmission in the months of January to Mayand low transmission in the months of June to December, asalready observed by Adetunji et al. (2007), and WHO (2007).The peak of measles disease in the study area occurs in themonth of March, with lowest occurrence in August.

The occurrence of measles disease with respect tometeorological/ biometeorological variables shows that

high occurrence of measles coincides with the period ofthe dry season while lower transmission occurs at the onsetof the rainy season. This pattern was noted throughout theduration of the study and is similar to the findings of Ferrariet al. (2008, 2010) in Niger. The seasonal forcing ofmeasles transmission in our study area is correlated withthermal comfort indices (PET, UTCI and Tmrt) and othermeteorological parameters such as air temperature, rainfall,solar radiation, SST and relative humid. The similaritybetween this study and that of Ferrari et al. (2008, 2010) isthat both studies show a correlation between cases ofmeasles and rainfall. The occurrence of measles hasremained consistent throughout the study period, withmeteorological/ biometeorological parameters being signif-icantly correlated with it.

Conclusion

Based on the findings of this study, it is obvious that socio-economic, poor hygiene/sanitation and environmental con-ditions are major driving factors responsible for the seasonalfluctuations in measles transmission in Ondo State, Nigeria.The results show a significant positive relationship betweensolar radiation, air temperature, SST, mean radiant tempera-ture, physiologically equivalent temperature and universalthermal climate index, while relative humidity and rainfallshow a significant negative relationship with measles for the11 years examined.

Thresholds have been identified for various meteorologicalparameters and biometeorological indices, above which therewill be high transmission/ prevalence of measles in the studyarea. Having observed the significant role of thermalcondition of the environment in the seasonal transmission ofmeasles, this study advocates the importance of inclusion of

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50

100

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200

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JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

PET,

UT

CI

& T

mrt

(ºC

)

Sola

r ra

diat

ion

(Wm

-2)

& R

epor

ted

mea

sles

cas

es

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MSL (Humid)SRPETUTCITmrt

Fig. 9 Relationship betweentrends of meteorological/biometeorological parametersand occurrence of measlesdisease for the period 1998–2008 in the humid forest zoneof Ondo state, Nigeria

Int J Biometeorol (2012) 56:873–885 883

thermal comfort indices in studies of the prevalence andtransmission of measles.

This study stresses the need for further investigation toexplore the role played by meteorological/ biometeorologicalfactors on a day-to-day basis in order to actually determine theweather transition period that has the most significant effect onmeasles disease in the study area. Such studies are all the morenecessary given that the aim of reducing measles case fatalityto near zero in Nigeria by the National Program onImmunisation has not been realised.

Finally, the implications of these results for the governmentand health policy decisions are numerous. First, that thereshould be adequate and standard infrastructure such as drains,sewers and waste disposal facilities in all the communities inthe study area so that problem of infectious diseases will bereduce because of the link between environmental conditionsand infectious diseases. In addition, the government shouldput in place policies that will no longer allow the rearing andco-habitation of livestock/domestic animals within humancommunities. Second, close monitoring of the atmosphericenvironment should be incorporated into health managementschemes; this will give health practitioners timely awarenessof the prevailing atmospheric conditions that are favourable tothe transmission of certain infectious diseases so that they canact in time. Third, since there are certain parts of the study areathat record higher numbers of measles cases, this couldindicate limited coverage of immunisation in those areas. It isnecessary, therefore, to prioritise health problems according toareas, and to direct health investments to areas most in need; ahuman-biometeorological approach will be of great help inachieving this. Fourth, there is need to protect our environ-ment in order not to be affected adversely by the impact ofclimate change.

Acknowledgements Thanks are due to the Alexander von HumboldtFoundation for the award of an International Climate Protection Fellowshipto A.G.O., and to the Monitoring and Evaluation Unit, Ministry of Healthand the Agroclimatological and Ecological Monitoring Unit of the OndoState Government for providing clinical and meteorological data,respectively. The authors would also like to thank the African Populationand Health Research Center (APHRC) and the International DevelopmentResearch Center (IDRC) for the award of African Doctoral DissertationResearch Fellowship to A.G.O.

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